Temperature has a significant impact on the production of edible mushrooms. The industrial production of edible mushrooms is committed to accurately maintaining the temperature inside the mushroom room within a certain range to achieve quality and efficiency improvement. However, current environmental regulation methods have problems such as lagging regulation and a large range of temperature fluctuations. There is an urgent need to accurately predict the temperature of mushroom houses in the future period to take measures in advance. Therefore, this article proposes a temperature prediction model for mushroom houses using a data–physical hybrid method. Firstly, the Boruta-SHAP algorithm was used to screen out the key influencing factors on the temperature of the mushroom room. Subsequently, the indoor temperature was decomposed using the optimized variational modal decomposition. Then, the gated recurrent unit neural network and attention mechanism were used to predict each modal component, and the mushroom house heat balance equation was incorporated into the model’s loss function. Finally, the predicted values of each component were accumulated to obtain the final result. The results demonstrated that integrating a simplified physical model into the predictive model based on data decomposition led to a 12.50% reduction in the RMSE of the model’s predictions compared to a purely data-driven model. The model proposed in this article exhibited good predictive performance in small datasets, reducing the time required for data collection in modeling.

The energy consumption of the mushroom room air conditioning system accounts for 40% of the total energy consumption of the mushroom factory. Efficient and energy-efficient mushroom factories and mushroom houses are the development direction of the industry. Compared with maintenance structure transformation and air conditioning equipment upgrading, energy-saving technology based on regulation methods has the advantages of less investment and fast effectiveness, which has attracted attention. The current methods for regulating air conditioning in edible mushroom factories include simple on/off thermostat control or PID. In the field of energy efficiency in commercial building air conditioning, a large number of studies have shown that compared with traditional control algorithms such as classic on/off or PID control, model predictive control can significantly improve energy efficiency. However, there is little literature mentioning the application of MPC in factory mushroom production rooms. This paper proposes a data-driven MPC and PID combined energy-saving control method for mushroom room air conditioning. This method uses the CNN-GRU-Attention combination neural network as the prediction model, combined with prediction error compensation and dynamic update mechanism of the prediction model dataset, to achieve an accurate prediction of indoor temperature in mushroom houses. Establish an objective function for air conditioning control duration and temperature, use the non-dominated sorting genetic algorithm II (NSGA-II) to solve for the optimal control sequence of the air conditioning in the control time domain, and use the entropy weight method to determine the optimal decision quantity. Integrate rolling optimization, feedback mechanism, and PID to achieve precise and energy-saving control of the mushroom room environment. The experimental results show that compared with the on/off thermostat and PID controller, the designed controller reduces power consumption by 12% and 5%, respectively, and has good application and demonstration value in the field of industrial production of edible mushrooms.

Load forecasting has significant implications on optimizing the operation of air conditioning systems for industrial mushroom houses and energy saving. This research paper presents a novel approach for short-term load forecasting in mushroom houses, which face challenges in accurately modeling cold and heat loads due to the complex interplay of various factors, including climatic conditions, mushroom growth, and equipment operation. The proposed method combines empirical wavelet transform (EWT), hybrid autoregressive integrated moving average (ARIMA), convolutional neural network (CNN), and bi-directional long short-term memory (BiLSTM) with an attention mechanism (CNN-BiLSTM-Attention) to address these challenges. The first step of this method was to select input features via the Boruta algorithm. Then, the EWT method was used to decompose the load data of mushroom houses into four modal components. Subsequently, the Lempel–Ziv method was introduced to classify the modal components into high-frequency and low-frequency classes. CNN-BiLSTM-Attention and ARIMA prediction models were constructed for these two classes, respectively. Finally, the predictions from both classes were combined and reconstructed to obtain the final load forecasting value. The experimental results show that the Boruta algorithm selects key influential feature factors effectively. Compared to the Spearman and Pearson correlation coefficient methods, the mean absolute error (MAE) of the prediction results is reduced by 14.72% and 3.75%, respectively. Compared to the ensemble empirical mode decomposition (EEMD) method, the EWT method can reduce the decomposition reconstruction error by an order of magnitude of ${10}^3$, effectively improving the accuracy of the prediction model. The proposed model in this paper exhibits significant advantages in prediction performance compared to the single neural network model, with the MAE, root mean square error (RMSE), and mean absolute percentage error (MAPE) of the prediction results reduced by 31.06%, 26.52%, and 39.27%, respectively.

This study puts forward a machine vision-based prediction method to solve the problem regarding the measurement of traits in shiitake mushroom caps during the shiitake mushroom breeding process. It enables precise phenotyping through accurate image acquisition and analysis. In practical applications, this method improves the breeding process by rapidly and non-invasively assessing key traits such as the size and color of shiitake mushroom caps, which helps in efficiently screening strains and reducing human errors. Firstly, an edge detection model was established. This model is called KL-Dexined. It achieved an per-image best threshold (OIS) rate of 93.5%. Also, it reached an Optimal Dynamic Stabilization (ODS) rate of 96.3%. Moreover, its Average Precision (AP) was 97.1%. Secondly, the edge information detected by KL-Dexined was mapped onto the original image of shiitake mushroom caps, and using the OpenCV model,11 phenotypic key features including shiitake mushroom caps area, perimeter, and external rectangular length were obtained. Experimental results demonstrated that the R² between predicted values and true values was 0.97 with an RMSE as low as 0.049. After conducting correlation analysis between phenotypic features and shiitake mushroom caps weight, four most correlated phenotypic features were identified: Area, Perimeter, External rectangular width, and Long axis; they were divided into four groups based on their correlation rankings. Finally,M3 group using GWO_SVM algorithm achieved optimal performance among six mainstream machine learning models tested with an R²value of 0.97 and RMSE only at 0.038 when comparing predicted values with true values. Hence, this study provided guidance for predicting key traits in shiitake mushroom caps.

In the heating, ventilation, and air conditioning (HVAC) systems of mushroom growing control rooms, traditional rule-based control methods are commonly adopted. However, these methods are characterized by response delays, leading to underutilization of energy-saving potential and energy costs that constitute a disproportionately high share of overall production costs. Therefore, minimizing the running time of the air conditioning system is crucial while maintaining the optimal growing environment for mushrooms. To address the aforementioned issues, this paper proposed a sensor optimization method based on the combination of principal component analysis (PCA) and information entropy. Furthermore, model predictive control (MPC) was implemented using a gated recurrent unit (GRU) neural network with an attention mechanism (GRU-Attention) as the prediction model to optimize the air conditioning system. First, a method combining PCA and information entropy was proposed to select the three most representative sensors from the 16 sensors in the mushroom room, thus eliminating redundant information and correlations. Then, a temperature prediction model based on GRU-Attention was adopted, with its hyperparameters optimized using the Optuna framework. Finally, an improved crayfish optimization algorithm (ICOA) was proposed as an optimizer for MPC. Its objective was to solve the control sequence with high accuracy and low energy consumption. The average energy consumption was reduced by approximately 11.2%, achieving a more stable temperature control effect.

Spatial-channel transformer network based on mask-RCNN for efficient mushroom instance segmentation